Visual SLAM technology for medical

Research Article
Open access

Visual SLAM technology for medical

Shanhua Chen 1*
  • 1 Zhejiang University    
  • *corresponding author 3210104421@zju.edu.cn
Published on 25 September 2023 | https://doi.org/10.54254/2755-2721/12/20230345
ACE Vol.12
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-013-4
ISBN (Online): 978-1-83558-014-1

Abstract

Simultaneous Localization and Mapping (SLAM) is a method to determine the location of a mobile robot in an unfamiliar environment with external information through the information obtained by the sensor and constructs a three-dimensional map of the unknown environment. There are two main types of SLAM: laser SLAM and visual SLAM. Visual SLAM technology uses image information as the only external information source, that is, to use the camera for pose estimation and map construction. SLAM technology using vision as an external information source is a very active research area with many excellent works. In recent years, visual SLAM has been widely used in the medical field, bringing many new methods. This thesis aims to absorb the extensive work on visual SLAM and its application in the medical field, and introduce their latest progress. And discuss the challenges and possible future development trends of visual SLAM technology in the medical field.

Keywords:

SLAM; medical applications; visual SLAM .

Chen,S. (2023). Visual SLAM technology for medical. Applied and Computational Engineering,12,220-224.
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References

[1]. A. Tourani, H. Bavle, J. L. Sanchez-Lopez, and H. Voos, “Visual SLAM: What Are the Current Trends and What to Expect?,” Sensors, vol. 22, no. 23, p. 9297, Nov. 2022, doi: 10.3390/s22239297.

[2]. A. Macario Barros, M. Michel, Y. Moline, G. Corre, and F. Carrel, “A Comprehensive Survey of Visual SLAM Algorithms,” Robotics, vol. 11, no. 1, p. 24, Feb. 2022, doi: 10.3390/robotics11010024.

[3]. T. Taketomi, H. Uchiyama, and S. Ikeda, “Visual SLAM algorithms: a survey from 2010 to 2016,” IPSJ T Comput Vis Appl, vol. 9, no. 1, p. 16, Dec. 2017, doi: 10.1186/s41074-017-0027-2.

[4]. J. Fuentes-Pacheco, J. Ruiz-Ascencio, and J. M. Rendón-Mancha, “Visual simultaneous localization and mapping: a survey,” Artif Intell Rev, vol. 43, no. 1, pp. 55–81, Jan. 2015, doi: 10.1007/s10462-012-9365-8.

[5]. O. G. Grasa, J. Civera, A. Guemes, V. Munoz, and J. M. M. Montiel, “EKF Monocular SLAM 3D Modeling, Measuring and Augmented Reality from Endoscope Image Sequences”.

[6]. O. G. Grasa, J. Civera, and J. M. M. Montiel, “EKF monocular SLAM with relocalization for laparoscopic sequences,” in 2011 IEEE International Conference on Robotics and Automation, Shanghai, China: IEEE, May 2011, pp. 4816–4821. doi: 10.1109/ICRA.2011.5980059.

[7]. P. K. Dixit, Y. Iwahori, M. K. Bhuyan, K. Kasugai, and A. Vishwakarma, “Polyp shape estimation from endoscopy video using EKF monocular SLAM with SFS model prior,” in 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai: IEEE, Mar. 2017, pp. 52–57. doi: 10.1109/WiSPNET.2017.8299718.

[8]. X. Liu, Z. Li, M. Ishii, G. D. Hager, R. H. Taylor, and M. Unberath, “SAGE: SLAM with Appearance and Geometry Prior for Endoscopy.” arXiv, Feb. 22, 2022. Accessed: May 15, 2023. [Online]. Available: http://arxiv.org/abs/2202.09487

[9]. R. Hartwig, D. Ostler, J.-C. Rosenthal, H. Feußner, D. Wilhelm, and D. Wollherr, “Constrained Visual-Inertial Localization With Application And Benchmark in Laparoscopic Surgery.” arXiv, Feb. 22, 2022.

[10]. Z. Yang, J. Pan, R. Li, and H. Qin, “Scene-graph-driven semantic feature matching for monocular digestive endoscopy,” Computers in Biology and Medicine, vol. 146, p. 105616, Jul. 2022, doi: 10.1016/j.compbiomed.2022.105616.

[11]. B. C. Becker and C. N. Riviere, “Real-time retinal vessel mapping and localization for intraocular surgery,” in 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany: IEEE, May 2013, pp. 5360–5365. doi: 10.1109/ICRA.2013.6631345.

Cite this article

Chen,S. (2023). Visual SLAM technology for medical. Applied and Computational Engineering,12,220-224.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume title: Proceedings of the 2023 International Conference on Mechatronics and Smart Systems

ISBN：978-1-83558-013-4(Print) / 978-1-83558-014-1(Online)
Editor：Seyed Ghaffar, Alan Wang
Conference website: https://2023.confmss.org/
Conference date: 24 June 2023
Series: Applied and Computational Engineering
Volume number: Vol.12
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. A. Tourani, H. Bavle, J. L. Sanchez-Lopez, and H. Voos, “Visual SLAM: What Are the Current Trends and What to Expect?,” Sensors, vol. 22, no. 23, p. 9297, Nov. 2022, doi: 10.3390/s22239297.

[2]. A. Macario Barros, M. Michel, Y. Moline, G. Corre, and F. Carrel, “A Comprehensive Survey of Visual SLAM Algorithms,” Robotics, vol. 11, no. 1, p. 24, Feb. 2022, doi: 10.3390/robotics11010024.

[3]. T. Taketomi, H. Uchiyama, and S. Ikeda, “Visual SLAM algorithms: a survey from 2010 to 2016,” IPSJ T Comput Vis Appl, vol. 9, no. 1, p. 16, Dec. 2017, doi: 10.1186/s41074-017-0027-2.

[4]. J. Fuentes-Pacheco, J. Ruiz-Ascencio, and J. M. Rendón-Mancha, “Visual simultaneous localization and mapping: a survey,” Artif Intell Rev, vol. 43, no. 1, pp. 55–81, Jan. 2015, doi: 10.1007/s10462-012-9365-8.

[5]. O. G. Grasa, J. Civera, A. Guemes, V. Munoz, and J. M. M. Montiel, “EKF Monocular SLAM 3D Modeling, Measuring and Augmented Reality from Endoscope Image Sequences”.

[6]. O. G. Grasa, J. Civera, and J. M. M. Montiel, “EKF monocular SLAM with relocalization for laparoscopic sequences,” in 2011 IEEE International Conference on Robotics and Automation, Shanghai, China: IEEE, May 2011, pp. 4816–4821. doi: 10.1109/ICRA.2011.5980059.

[7]. P. K. Dixit, Y. Iwahori, M. K. Bhuyan, K. Kasugai, and A. Vishwakarma, “Polyp shape estimation from endoscopy video using EKF monocular SLAM with SFS model prior,” in 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai: IEEE, Mar. 2017, pp. 52–57. doi: 10.1109/WiSPNET.2017.8299718.

[8]. X. Liu, Z. Li, M. Ishii, G. D. Hager, R. H. Taylor, and M. Unberath, “SAGE: SLAM with Appearance and Geometry Prior for Endoscopy.” arXiv, Feb. 22, 2022. Accessed: May 15, 2023. [Online]. Available: http://arxiv.org/abs/2202.09487

[9]. R. Hartwig, D. Ostler, J.-C. Rosenthal, H. Feußner, D. Wilhelm, and D. Wollherr, “Constrained Visual-Inertial Localization With Application And Benchmark in Laparoscopic Surgery.” arXiv, Feb. 22, 2022.

[10]. Z. Yang, J. Pan, R. Li, and H. Qin, “Scene-graph-driven semantic feature matching for monocular digestive endoscopy,” Computers in Biology and Medicine, vol. 146, p. 105616, Jul. 2022, doi: 10.1016/j.compbiomed.2022.105616.

[11]. B. C. Becker and C. N. Riviere, “Real-time retinal vessel mapping and localization for intraocular surgery,” in 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany: IEEE, May 2013, pp. 5360–5365. doi: 10.1109/ICRA.2013.6631345.

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